As has been well-researched in various behavioral sciences and is repeatedly observed in real life, the human mind is rarely completely rational. Instead, it succumbs to hidden assumptions and has numerous biases and preconceptions that cloud and color thinking and decision making.
These deficiencies can cause dangerous problems. For example, in the area of intelligence analysis, repeatedly, over time, surprise attacks have occurred. Congressional investigations have blamed a cause of the 9/11 attacks, as well as other calamities, on bias and faulty assumptions. There is a need to reduce the potential for dangerous events such as these.
In another field, evaluating financial value and predicting financial numbers is fraught with risks. Capital budgeting, acquisitions, and sourcing decisions might end up in error by large sums of money. Experts in the field are well aware of these difficulties and there is a need to remedy these difficulties.
A variety of decision support tools have been developed to help users make better decisions in risky and uncertain situations, including: optimization programs, statistical analyses, simulations, and an array of data mining and artificial analysis techniques. These previous efforts suffer from a lack of ease of use, lack of consideration of important factors, and various other drawbacks.